Experiments in terabyte searching, genomic retrieval and novelty detection for TREC 2004

In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year to support our experiments into large scale, distributed information retrieval, which underlies all of the track experiments described in this document

Retrieval from full-text medical literature: the dream & the reality

pre-printWhile the retrieval of the full-text of a document might seem to end all the hassle of using traditional retrieval systems, the results of the MEDLINE/Full-Text Project indicate that retrieval from the current full-text databases of biomedical journal literature does not match the dream. During the first phase of the project the researchers learned that searching the full-text databases resulted in the retrieval of a significantly larger number of relevant documents than MEDLINE. However, the full-text databases also resulted in a large number of non-relevant documents. Currently the researchers are focusing on how to search these databases to continue retrieving the large number of relevant documents but without so many non-relevant items

Multilingual query expansion in the Svemed+ bibliographic database : a case study

SveMed+ is a bibliographic database covering Scandinavian medical journals. It is produced by the University Library of Karolinska Institutet in Sweden. The bibliographic references are indexed with terms from the Medical Subject Headings (MeSH) thesaurus. The MeSH has been translated into several languages including Swedish, making it suitable as the basis for multilingual tools in the medical field. The data structure of SveMed+ closely mimics that of PubMed/MEDLINE. Users of PubMed/MEDLINE and similar databases typically expect retrieval features that are not readily available off-the-shelf. The SveMed+ interface is based on a free text search engine (Solr) and a relational database management system (Microsoft SQL Server) containing the bibliographic database and a multilingual thesaurus database. The thesaurus database contains medical terms in three different languages and information about relationships between the terms. A combined approach involving the Solr free text index, the bibliographic database and the thesaurus database allowed the implementation of functionality such as automatic multilingual query expansion, faceting and hierarchical explode searches. The present paper describes how this was done in practice.NoneAccepte

MedlinePlus??: The National Library of Medicine?? Brings Quality Information to Health Consumers

The National Library of Medicine???s (NLM??) MedlinePlus?? is a high-quality gateway to consumer health information from NLM, the National Institutes of Health (NIH), and other authoritative organizations. For decades, NLM has been a leader in indexing, organizing, and distributing health information to health professionals. In creating MedlinePlus, NLM uses years of accumulated expertise and technical knowledge to produce an authoritative, reliable consumer health Web site. This article describes the development of MedlinePlus???its quality control processes, the integration of NLM and NIH information, NLM???s relationship to other institutions, the technical and staffing infrastructures, the use of feedback for quality improvement, and future plans.published or submitted for publicatio

Boolean versus ranked querying for biomedical systematic reviews

Background: The process of constructing a systematic review, a document that compiles the published evidence pertaining to a specified medical topic, is intensely time-consuming, often taking a team of researchers over a year, with the identification of relevant published research comprising a substantial portion of the effort. The standard paradigm for this information-seeking task is to use Boolean search; however, this leaves the user(s) the requirement of examining every returned result. Further, our experience is that effective Boolean queries for this specific task are extremely difficult to formulate and typically require multiple iterations of refinement before being finalized. Methods: We explore the effectiveness of using ranked retrieval as compared to Boolean querying for the purpose of constructing a systematic review. We conduct a series of experiments involving ranked retrieval, using queries defined methodologically, in an effort to understand the practicalities of incorporating ranked retrieval into the systematic search task. Results: Our results show that ranked retrieval by itself is not viable for this search task requiring high recall. However, we describe a refinement of the standard Boolean search process and show that ranking within a Boolean result set can improve the overall search performance by providing early indication of the quality of the results, thereby speeding up the iterative query-refinement process. Conclusions: Outcomes of experiments suggest that an interactive query-development process using a hybrid ranked and Boolean retrieval system has the potential for significant time-savings over the current search process in the systematic reviewing

Errors in search strategies used in systematic reviews and their effects on information retrieval

Objectives: Errors in search strategies negatively affect the quality and validity of systematic reviews. The primary objective of this study was to evaluate searches performed in MEDLINE/PubMed to identify errors and determine their effects on information retrieval. Methods: A PubMed search was conducted using the systematic review filter to identify articles that were published in January of 2018. Systematic reviews or meta-analyses were selected from a systematic search for literature containing reproducible and explicit search strategies in MEDLINE/PubMed. Data were extracted from these studies related to ten types of errors and to the terms and phrases search modes. Results: The study included 137 systematic reviews in which the number of search strategies containing some type of error was very high (92.7%). Errors that affected recall were the most frequent (78.1%), and the most common search errors involved missing terms in both natural language and controlled language and those related to Medical Subject Headings (MeSH) search terms and the non-retrieval of their more specific terms. Conclusions: To improve the quality of searches and avoid errors, it is essential to plan the search strategy carefully, which includes consulting the MeSH database to identify the concepts and choose all appropriate terms, both descriptors and synonyms, and combining search techniques in the free-text and controlled-language fields, truncating the terms appropriately to retrieve all their variants

Identifying quality improvement intervention publications - A comparison of electronic search strategies

Abstract Background The evidence base for quality improvement (QI) interventions is expanding rapidly. The diversity of the initiatives and the inconsistency in labeling these as QI interventions makes it challenging for researchers, policymakers, and QI practitioners to access the literature systematically and to identify relevant publications. Methods We evaluated search strategies developed for MEDLINE (Ovid) and PubMed based on free text words, Medical subject headings (MeSH), QI intervention components, continuous quality improvement (CQI) methods, and combinations of the strategies. Three sets of pertinent QI intervention publications were used for validation. Two independent expert reviewers screened publications for relevance. We compared the yield, recall rate, and precision of the search strategies for the identification of QI publications and for a subset of empirical studies on effects of QI interventions. Results The search yields ranged from 2,221 to 216,167 publications. Mean recall rates for reference publications ranged from 5% to 53% for strategies with yields of 50,000 publications or fewer. The 'best case' strategy, a simple text word search with high face validity ('quality' AND 'improv*' AND 'intervention*') identified 44%, 24%, and 62% of influential intervention articles selected by Agency for Healthcare Research and Quality (AHRQ) experts, a set of exemplar articles provided by members of the Standards for Quality Improvement Reporting Excellence (SQUIRE) group, and a sample from the Cochrane Effective Practice and Organization of Care Group (EPOC) register of studies, respectively. We applied the search strategy to a PubMed search for articles published in 10 pertinent journals in a three-year period which retrieved 183 publications. Among these, 67% were deemed relevant to QI by at least one of two independent raters. Forty percent were classified as empirical studies reporting on a QI intervention. Conclusions The presented search terms and operating characteristics can be used to guide the identification of QI intervention publications. Even with extensive iterative development, we achieved only moderate recall rates of reference publications. Consensus development on QI reporting and initiatives to develop QI-relevant MeSH terms are urgently needed

Hybrid Query Expansion on Ontology Graph in Biomedical Information Retrieval

Nowadays, biomedical researchers publish thousands of papers and journals every day. Searching through biomedical literature to keep up with the state of the art is a task of increasing difficulty for many individual researchers. The continuously increasing amount of biomedical text data has resulted in high demands for an efficient and effective biomedical information retrieval (BIR) system. Though many existing information retrieval techniques can be directly applied in BIR, BIR distinguishes itself in the extensive use of biomedical terms and abbreviations which present high ambiguity. First of all, we studied a fundamental yet simpler problem of word semantic similarity. We proposed a novel semantic word similarity algorithm and related tools called Weighted Edge Similarity Tools (WEST). WEST was motivated by our discovery that humans are more sensitive to the semantic difference due to the categorization than that due to the generalization/specification. Unlike most existing methods which model the semantic similarity of words based on either the depth of their Lowest Common Ancestor (LCA) or the traversal distance of between the word pair in WordNet, WEST also considers the joint contribution of the weighted distance between two words and the weighted depth of their LCA in WordNet. Experiments show that weighted edge based word similarity method has achieved 83.5% accuracy to human judgments. Query expansion problem can be viewed as selecting top k words which have the maximum accumulated similarity to a given word set. It has been proved as an effective method in BIR and has been studied for over two decades. However, most of the previous researches focus on only one controlled vocabulary: MeSH. In addition, early studies find that applying ontology won\u27t necessarily improve searching performance. In this dissertation, we propose a novel graph based query expansion approach which is able to take advantage of the global information from multiple controlled vocabularies via building a biomedical ontology graph from selected vocabularies in Metathesaurus. We apply Personalized PageRank algorithm on the ontology graph to rank and identify top terms which are highly relevant to the original user query, yet not presented in that query. Those new terms are reordered by a weighted scheme to prioritize specialized concepts. We multiply a scaling factor to those final selected terms to prevent query drifting and append them to the original query in the search. Experiments show that our approach achieves 17.7% improvement in 11 points average precision and recall value against Lucene\u27s default indexing and searching strategy and by 24.8% better against all the other strategies on average. Furthermore, we observe that expanding with specialized concepts rather than generalized concepts can substantially improve the recall-precision performance. Furthermore, we have successfully applied WEST from the underlying WordNet graph to biomedical ontology graph constructed by multiple controlled vocabularies in Metathesaurus. Experiments indicate that WEST further improve the recall-precision performance. Finally, we have developed a Graph-based Biomedical Search Engine (G-Bean) for retrieving and visualizing information from literature using our proposed query expansion algorithm. G-Bean accepts any medical related user query and processes them with expanded medical query to search for the MEDLINE database

Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena